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Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter
The first large-scale validation of the "complex contagion" principle from sociology, which posits that repeated exposures to an idea are particularly crucial when the idea is in some way controversial or contentious, is provided.
A Trainable Spaced Repetition Model for Language Learning
HLR combines psycholinguistic theory with modern machine learning techniques, indirectly estimating the “halflife” of a word or concept in a student’s long-term memory, reducing error by 45%+ compared to several baselines at predicting student recall rates.
PICS: Parameter-free Identification of Cohesive Subgroups in Large Attributed Graphs
This work proposes PICS, a novel, parameter-free method for mining attributed graphs that requires no user-specified parameters such as the number of clusters and similarity functions, and its running time scales linearly with total graph and attribute size.
Predicting Reciprocity in Social Networks
- Justin Cheng, Daniel M. Romero, Brendan Meeder, J. Kleinberg
- Computer ScienceIEEE Third Int'l Conference on Privacy, Security…
- 1 October 2011
This paper extracts a network based on directed@-messages sent between users on Twitter, and identifies measures based on the attributes of nodes and their network neighborhoods that can be used to construct good predictors of reciprocity.
IWantPrivacy : Widespread Violation of Privacy Settings in the Twitter Social Network
A large-scale collection of data from the Twitter social network is performed by means of the publicly available application programming interface (API) they provide and reveals the growing trend where users defeat Twitter’s simple privacy mechanism of “protecting one's tweets” by retweeting a protected tweet.
Spectral Analysis for Billion-Scale Graphs: Discoveries and Implementation
The proposed HEIGEN algorithm is carefully design to be accurate, efficient, and able to run on the highly scalable MAPREDUCE (HADOOP) environment, which enables Heiden to handle matrices more than 1000× larger than those which can be analyzed by existing algorithms.
We know who you followed last summer: inferring social link creation times in twitter
- Brendan Meeder, B. Karrer, Amin S. Sayedi-Roshkhar, R. Ravi, C. Borgs, J. Chayes
- Computer ScienceWWW
- 28 March 2011
It is concluded from this analysis that real-world events and changes to Twitter's interface for recommending users strongly influence network growth, and a single static snapshot can give novel insights about Twitter's evolution.
Measuring Online Service Availability Using Twitter
- Marti A. Motoyama, Brendan Meeder, Kirill Levchenko, G. Voelker, S. Savage
- Computer ScienceWOSN
- 22 June 2010
It is shown that simple techniques are sufficient to extract key semantic content from "tweets" (i.e., service X is down) and also filter out extraneous noise and the efficacy of this approach at identifying a range of large-scale service outages in 2009 for popular services such as Gmail, Bing and PayPal is demonstrated.
Black Twitter: Building Connection through Cultural Conversation
There’s power in these Black Twitter streets...motivating masses to do anything creates something. There’s always results. A lot of times on Twitter there are just a lot of words, but then something…
HEigen: Spectral Analysis for Billion-Scale Graphs
- U. Kang, Brendan Meeder, E. Papalexakis, C. Faloutsos
- Computer ScienceIEEE Transactions on Knowledge and Data…
- 1 February 2014
The proposed HEIGEN algorithm is carefully design to be accurate, efficient, and able to run on the highly scalable MAPREDUCE (HADOOP) environment, which enablesHEIGEN to handle matrices more than 1;000 × larger than those which can be analyzed by existing algorithms.